Proximate_Analysis

This database studies the performance inconsistency on the biomass HHV proximate analysis. The research null hypothesis is the consistency in the rank of a biomass HHV model. Fifteen biomass models are trained and tested in four datasets. In each dataset, the rank invariability of these 15 models indicates the performance consistency. The database includes the datasets and source codes to analyze the performance consistency of the biomass HHV. These datasets are stored in tabular on an excel workbook. The source codes are the biomass HHV machine learning model through the MATLAB Objected Orient Program (OOP). These models consist of eight regressions, four supervised learnings, and three neural networks. An excel workbook, "BiomassDataSetProximate.xlsx," collects the research datasets in six worksheets. The first worksheet, "Proximate," contains 803 HHV data from 17 pieces of literature. The names of the worksheet column indicate the elements of the proximate analysis on a % dry basis. The HHV column refers to the higher heating value in MJ/kg. The following worksheet, "Full Residuals," backups the model testing's residuals based on the 20-fold cross-validations. The article verifies the performance consistency through these residuals. The other worksheets present the literature datasets implemented to train and test the model performance in many pieces of literature. A file named "SourceCodeProximate.rar" collects the MATLAB machine learning models implemented in the article. The list of the folders in this file is the class structure of the machine learning models. These classes extend the features of the original MATLAB's Statistics and Machine Learning Toolbox to support, e.g., the k-fold cross-validation. The MATLAB script, "runStudyProximate.m," is the article's main program (Kijkarncharoensin & Innet, 2021) to analyze the performance consistency of the biomass HHV model through the proximate analysis. The script instantly loads the datasets from the excel workbook and automatically fits the biomass model through the OOP classes. The first section of the MATLAB script generates the most accurate model by optimizing the model's higher parameters. It takes a few hours for the first run to train the machine learning model via the trial and error process. The trained models can be saved in MATLAB .mat file and loaded back to the MATLAB workspace. The remaining script, separated by the script section break, performs the residual analysis to inspect the performance consistency. Furthermore, the figure of the biomass data in the 3D scatter plot, and the box plots of the prediction residuals are exhibited. Finally, the interpretations of these results are examined in the author's article. Reference : Kijkarncharoensin, A., & Innet, S. (2021). Performance inconsistency of the Biomass Higher Heating Value (HHV) Models derived from Proximate Analysis [Manuscript in preparation]. University of the Thai Chamber of Commerce.本数据库针对生物质高热值（Higher Heating Value, HHV）工业分析（proximate analysis）中的性能不一致性展开研究。本研究的原假设为生物质HHV模型的排序具有一致性。研究在4个数据集上对15种生物质模型进行训练与测试，以这15种模型的排序稳定性作为性能一致性的判定依据。 本数据库包含用于分析生物质HHV性能一致性的数据集与源代码。数据集以表格形式存储于Excel工作簿中；源代码为基于MATLAB面向对象编程（Object-Oriented Programming, OOP）实现的生物质HHV机器学习模型，涵盖8种回归模型、4种监督学习模型与3种神经网络模型。 名为"BiomassDataSetProximate.xlsx"的Excel工作簿在6个工作表中收录了本研究的数据集。首个工作表“Proximate”包含来自17篇文献的803组HHV数据，工作表列名以干基百分比标注了工业分析的各项组分，“HHV”列代表以MJ/kg为单位的高热值。第二个工作表“Full Residuals”备份了基于20折交叉验证（20-fold cross-validation）得到的模型测试残差，本文通过这些残差验证性能一致性。其余工作表则收录了多篇文献中用于训练与测试模型性能的公开数据集。 名为"SourceCodeProximate.rar"的压缩包收录了本文中使用的MATLAB机器学习模型。该压缩包内的文件夹列表对应机器学习模型的类结构，这些类扩展了MATLAB原生统计与机器学习工具箱（Statistics and Machine Learning Toolbox）的功能，以支持如k折交叉验证等操作。MATLAB脚本"runStudyProximate.m"为本研究的核心程序（Kijkarncharoensin & Innet, 2021），用于通过工业分析数据探究生物质HHV模型的性能一致性。该脚本可直接从Excel工作簿加载数据集，并通过OOP类自动拟合生物质模型。 MATLAB脚本的第一部分通过优化模型高阶参数生成精度最优的模型。首次运行时，需通过试错流程训练机器学习模型，耗时约数小时。训练完成的模型可保存为MATLAB .mat文件，并可重新加载至MATLAB工作区。脚本其余部分以分段分隔符划分，用于执行残差分析以检验性能一致性，此外还将生成生物质数据的三维散点图与预测残差箱线图。最终研究结果的解读详见作者发表的论文。 参考文献： Kijkarncharoensin, A., & Innet, S. (2021). 基于工业分析的生物质高热值（HHV）模型性能不一致性 [Manuscript in preparation（待出版手稿）]. 泰国商会大学。